Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add inflexa-ai/inflexa --skill cheminformaticsgit clone --depth 1 https://github.com/inflexa-ai/inflexaWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/inflexa-ai/inflexa/cheminformatics)<a href="https://agentmods.dev/skills/inflexa-ai/inflexa/cheminformatics"><img src="https://agentmods.dev/badge/skills/inflexa-ai/inflexa/cheminformatics/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/inflexa-ai/inflexa/cheminformatics"><img src="https://agentmods.dev/badge/skills/inflexa-ai/inflexa/cheminformatics.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00044 | $0.04446 |
| Opus 5 | $0.00022 | $0.02223 |
| Sonnet 5 | $0.00009 | $0.00889 |
| Haiku 4.5 | $0.00004 | $0.00445 |
Grade A, and why
cheminformatics scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 258 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cheminformatics
This skill guides method selection and execution for molecular structure analysis, structure-activity relationship (SAR) triage, compound library profiling, QSAR modeling, ADMET prediction, and chemical space visualization. All workflows assume Python with RDKit as the core cheminformatics toolkit, supplemented by datamol, mordred, and DeepChem for specialized tasks.
Method Selection Decision Tree
Choose the method based on your input data and analytical goal:
-
SAR Triage (has structures + activity data)
- Murcko scaffold decomposition via
rdkit.Chem.Scaffolds.MurckoScaffold-- group compounds by core ring system. - Property profiling via
rdkit.Chem.Descriptors+datamol-- MW, logP, TPSA, HBD, HBA, RotBonds for each compound. - Druglikeness assessment: Lipinski Ro5 (MW<=500, logP<=5, HBD<=5, HBA<=10), Veber (TPSA<=140, RotBonds<=10), Ghose rules.
- Structural alert screening via
rdkit.Chem.FilterCatalog-- PAINS, Brenk, NIH catalogs. - Activity distribution analysis:
pandas+scipy.statsfor distribution statistics, outlier detection, and potency binning. - Scaffold-activity cross-tab: scaffold x potency matrix to identify chemotypes with best activity profiles.
- Murcko scaffold decomposition via
-
Library Profiling (structures only, no activity data)
- Property distributions: MW, logP, TPSA, HBD, HBA, RotBonds -- histogram/violin for each, Ro5 reference lines overlaid.
- Chemical diversity: Tanimoto similarity on Morgan fingerprints (radius=2, 2048 bits) + Butina clustering (cutoff=0.4).
- Chemical space map: UMAP or t-SNE on Morgan fingerprints, colored by cluster assignment.
- Structural alert sweep: PAINS, Brenk, NIH catalogs -- report clean percentage and most common alert types.
-
Compound Comparison (two or more compound sets)
- Property comparison: violin/box plots per set for MW, logP, TPSA, HBD, HBA, RotBonds -- Mann-Whitney U or KS test for statistical comparison.
- Scaffold overlap: Venn diagram on Murcko scaffolds (generic framework) to show shared vs unique chemotypes.
- Chemical space overlap: UMAP on combined fingerprint matrix, colored by set membership, with convex hulls or density contours.
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- today First seen · 258 lines · 44 tokens per session scan A e55102171ce1
cheminformatics is a skill published in the GitHub repository inflexa-ai/inflexa (33 stars, last pushed yesterday), licensed Apache-2.0. It adds 44 tokens to every session and 4,446 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-09.
Other skills, from other repositories
cheminformatics
Cheminformatics pipeline for small-molecule property calculation, filtering, and similarity analysis using RDKit. Use when the user asks to compute molecular descriptors, filter compounds by Lipinski or Veber rules, detect PAINS, calculate fingerprint similarity, run matched molecular pair analysis, generate ADMET…
rdkit-cheminformatics
Use this Skill for cheminformatics with RDKit: SMILES/InChI parsing, Morgan fingerprints, Tanimoto similarity, Murcko scaffold decomposition, substructure search, and chemical space visualization.
datamol
Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery including SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters…
rdkit
Cheminformatics toolkit for fine-grained molecular control. SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure search, 2D/3D generation, similarity, reactions. For standard workflows with simpler interface, use datamol (wrapper around RDKit). Use rdkit for advanced control, custom…
drug-lead-analysis
Analyze drug candidate molecules for drug-likeness, ADMET properties, and safety profiles. Use this skill when: (1) Evaluating a molecule's potential as a drug candidate, (2) Checking drug-likeness scores (QED, Lipinski), (3) Predicting blood-brain barrier penetration, (4) Assessing side effects and ADMET properties…
chembl-bioactivity
Use this Skill to query ChEMBL for bioactivity data: target lookup, IC50/Ki retrieval, activity cliffs, SAR tables, and pChEMBL-normalized values.